Soil Unconfined Compressive Strength Prediction Using Random Forest (RF) Machine Learning Model

Hai-Bang Ly,Binh Thai Pham

doi:10.2174/1874836802014010278

Abstract

Aims: Understanding the mechanical performance and applicability of soils is crucial in geotechnical engineering applications. This study investigated the possibility of application of the Random Forest (RF) algorithm – a popular machine learning method to predict the soil unconfined compressive strength (UCS), which is one of the most important mechanical properties of soils. Methods: A total number of 118 samples collected and their tests derived from the laboratorial experiments carried out under the Long Phu 1 power plant project, Vietnam. Data used for modeling includes clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit as input variables, whereas the target is the UCS. Several assessment criteria were used for evaluating the RF model, namely the correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE). Results: The results showed that RF exhibited a strong capability to predict the UCS, with the R value of 0.914 and 0.848 for the training and testing datasets, respectively. Finally, a sensitivity analysis was conducted to reveal the importance of input parameters to the prediction of UCS using RF. The specific gravity was found as the most affecting variable, following by clay content, liquid limit, plastic limit, moisture content and void ratio. Conclusion: This study might help in the accurate and quick prediction of the UCS for practice purpose.

Highlights

Soil science is a complex discipline that involves fundamental and applied aspects of soil biology, soil physics and soil chemistry [1]
Random Forest (RF), a well-known supervised machine learning algorithm, is a nonparametric technique derived from classification and regression trees (CART), which applies ensemble learning method to solve problems [29]
RF is referred to construction of many trees, where each tree is generated by bootstrap samples

Summary

Introduction

Soil science is a complex discipline that involves fundamental and applied aspects of soil biology, soil physics and soil chemistry [1]. In civil engineering, understanding the mechanical properties of soils in a relationship with the applications is of fundamental importance [2]. Soil Unconfined Compressive Strength (UCS) is an important factor which is used to validate the compaction ability of soil [9]. It can be directly determined in the laboratory through unconfined compression test. The use of machine learning algorithms has spread rapidly over the last decades, especially in computer science.

Methods

Results

Conclusion